Browsing by Author "MEKONEN MOKE WAGARA"

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    CONTEXT-BASED SPELL CHECKER FOR SIDAAMU AFOO
    (Hawassa University, 2022-03-04) MEKONEN MOKE WAGARA
    A spell checker is one of the applications of natural language processing that is used to detect and correct spelling errors in written text. Spelling errors that occur in the written text can be non-word errors or real-word errors. A non-word error is a misspelled word that is not found in the language and has no meaning whereas a real-word error, that is, the word is a valid word in the language but it does not fit contextually in the sentence. We designed and implemented a spell checker for Sidaamu Afoo that can detect and correct both non-word and real-word errors. Sidaamu Afoo is one of the languages spoken in the Sidaama region in the south-central part of Ethiopia. It is an official working language and is used as a medium of instruction in primary schools of the Sidaama national regional state in Ethiopia. To address the issue of spelling errors in the Sidaamu Afoo text, a spell checker is required. In this study, the dictionary look-up approach with a hashing algorithm is used to detect non-word errors, and the character-based encoder-decoder model is used to correct the non-word errors. The LSTM model with attention mechanism and edit distance is used to detect and correct the context based spelling error. To conduct the experiment, 55440 sentences were used, of which 90% were for training (i.e., 49,896) and 10% were for testing (i.e., 5544). According to the experimental results, for an isolated spell checker, dictionary lookup with hashing achieved an accuracy of 93.05%, a recall of correct words of 91.51%, and a precision of incorrect words of 72.37% for detection. The encoder decoder model achieved a recall of 91.76% for corrections. For a context-sensitive spell checker, the LSTM model with attention and edit distance achieved an accuracy of 88.8%, recall of the correct word of 86.18%, and precision of the incorrect word of 62.84% for detection. It achieved a recall of 74.28% for the correction. The results of the experiment show that the model used to detect and correct both non-word and real-word spelling errors in Sidaamu Afoo’s written text performed well. Finally, to improve the performance of the model, we recommend using additional data set and a state-of-the-art transformer model.